The 'implementation' and use of smart home technology to lengthen independent living of non-instutionalized elderly have not always been flawless. The purpose of this study is to show that problems with smart home technology can be partially ascribed to differences in perception of the stakeholders involved. The perceptual worlds of caregivers, care receivers, and designers vary due to differences in background and experiences. To decrease the perceptual differences between the stakeholders, we propose an analysis of the expected and experienced effects of smart home technology for each group. For designers the effects will involve effective goals, caregivers are mainly interested in effects on workload and quality of care, while care receivers are influenced by usability effects. Making each stakeholder aware of the experienced and expected effects of the other stakeholders may broaden their perspectives and may lead to more successful implementations of smart home technology, and technology in general.
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Expectations are high with regards to smart home technology. In particular, smart home technology is expected to support or enable independent living by older adults. This raises the question: can smart home technology contribute to independent living, according to older adults themselves? This chapter aims to answer this question by reviewing and discussing older adults’ perspectives on independence and their views on smart home technology. Firstly, older adults’ opinions on independence and aging in place are discussed. Secondly, this chapter will review to what extent smart home technology can support older adults’ independence. Thirdly, it will be explained how community-dwelling older adults’ concept of independence entails three distinct types or modes, and how these modes are related to their perceptions and acceptance of technology. In the last section of this chapter, an overview of key points is presented, and recommendations for technology designers, policy makers, and care providers are postulated.
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The effectiveness of smart home technology in home care situations depends on the acceptance and use of the technology by both users and end-users. In the Netherlands many projects have started to introduce smart home technology and telecare in the homes of elderly people, but only some have been successful. In this paper, features for success and failure in the deployment of new (ICT) technology in home care are used to revise the technology acceptance model (TAM) into a model that explains the use of smart home and telecare technology by older adults. In the revised model we make the variable 'usefulness' more specific, by describing the benefits of the technology that are expected to positively affect technology usage. Additionally, we state that several moderator variables - that are expected to influence this effect - should be added to the model in order to explain why people eventually do (not) use smart home technology, despite the benefits and the intention to use. We categorize these variables, that represent the problems found in previous studies, in 'accessibility', 'facilitating conditions' and 'personal variables'.
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Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
